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Ensemble Learning Approach for Probabilistic Forecasting of Solar Power Generation

Author

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  • Azhar Ahmed Mohammed

    (Department of Electrical Engineering and Computer Science, Masdar Institute of Science and Technology, 54224 Abu Dhabi, UAE)

  • Zeyar Aung

    (Department of Electrical Engineering and Computer Science, Masdar Institute of Science and Technology, 54224 Abu Dhabi, UAE)

Abstract

Probabilistic forecasting accounts for the uncertainty in prediction that arises from inaccurate input data due to measurement errors, as well as the inherent inaccuracy of a prediction model. Because of the variable nature of renewable power generation depending on weather conditions, probabilistic forecasting is well suited to it. For a grid-tied solar farm, it is increasingly important to forecast the solar power generation several hours ahead. In this study, we propose three different methods for ensemble probabilistic forecasting, derived from seven individual machine learning models, to generate 24-h ahead solar power forecasts. We have shown that while all of the individual machine learning models are more accurate than the traditional benchmark models, like autoregressive integrated moving average (ARIMA), the ensemble models offer even more accurate results than any individual machine learning model alone does. Furthermore, it is observed that running separate models on the data belonging to the same hour of the day vastly improves the accuracy of the results. Getting more accurate forecasts will help the stakeholders come up with better decisions in resource planning and control when large-scale solar farms are integrated into the power grid.

Suggested Citation

  • Azhar Ahmed Mohammed & Zeyar Aung, 2016. "Ensemble Learning Approach for Probabilistic Forecasting of Solar Power Generation," Energies, MDPI, vol. 9(12), pages 1-17, December.
  • Handle: RePEc:gam:jeners:v:9:y:2016:i:12:p:1017-:d:84169
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    References listed on IDEAS

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    1. Zhaoxuan Li & SM Mahbobur Rahman & Rolando Vega & Bing Dong, 2016. "A Hierarchical Approach Using Machine Learning Methods in Solar Photovoltaic Energy Production Forecasting," Energies, MDPI, vol. 9(1), pages 1-12, January.
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    Cited by:

    1. Sabarathinam Srinivasan & Suresh Kumarasamy & Zacharias E. Andreadakis & Pedro G. Lind, 2023. "Artificial Intelligence and Mathematical Models of Power Grids Driven by Renewable Energy Sources: A Survey," Energies, MDPI, vol. 16(14), pages 1-56, July.
    2. Hassan, Muhammed A. & Al-Ghussain, Loiy & Ahmad, Adnan Darwish & Abubaker, Ahmad M. & Khalil, Adel, 2022. "Aggregated independent forecasters of half-hourly global horizontal irradiance," Renewable Energy, Elsevier, vol. 181(C), pages 365-383.
    3. Ahmed, R. & Sreeram, V. & Mishra, Y. & Arif, M.D., 2020. "A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 124(C).
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    5. Jebli, Imane & Belouadha, Fatima-Zahra & Kabbaj, Mohammed Issam & Tilioua, Amine, 2021. "Prediction of solar energy guided by pearson correlation using machine learning," Energy, Elsevier, vol. 224(C).
    6. Takuji Matsumoto & Yuji Yamada, 2021. "Comprehensive and Comparative Analysis of GAM-Based PV Power Forecasting Models Using Multidimensional Tensor Product Splines against Machine Learning Techniques," Energies, MDPI, vol. 14(21), pages 1-22, November.
    7. Andi A. H. Lateko & Hong-Tzer Yang & Chao-Ming Huang & Happy Aprillia & Che-Yuan Hsu & Jie-Lun Zhong & Nguyễn H. Phương, 2021. "Stacking Ensemble Method with the RNN Meta-Learner for Short-Term PV Power Forecasting," Energies, MDPI, vol. 14(16), pages 1-23, August.
    8. Seungbeom Nam & Jin Hur, 2018. "Probabilistic Forecasting Model of Solar Power Outputs Based on the Naïve Bayes Classifier and Kriging Models," Energies, MDPI, vol. 11(11), pages 1-15, November.
    9. Luca Massidda & Marino Marrocu, 2018. "Quantile Regression Post-Processing of Weather Forecast for Short-Term Solar Power Probabilistic Forecasting," Energies, MDPI, vol. 11(7), pages 1-20, July.
    10. Mustafa Jaihuni & Jayanta Kumar Basak & Fawad Khan & Frank Gyan Okyere & Elanchezhian Arulmozhi & Anil Bhujel & Jihoon Park & Lee Deog Hyun & Hyeon Tae Kim, 2020. "A Partially Amended Hybrid Bi-GRU—ARIMA Model (PAHM) for Predicting Solar Irradiance in Short and Very-Short Terms," Energies, MDPI, vol. 13(2), pages 1-20, January.
    11. Promphak Dawan & Kobsak Sriprapha & Songkiate Kittisontirak & Terapong Boonraksa & Nitikorn Junhuathon & Wisut Titiroongruang & Surasak Niemcharoen, 2020. "Comparison of Power Output Forecasting on the Photovoltaic System Using Adaptive Neuro-Fuzzy Inference Systems and Particle Swarm Optimization-Artificial Neural Network Model," Energies, MDPI, vol. 13(2), pages 1-18, January.
    12. Francisco Martínez-Álvarez & Alicia Troncoso & José C. Riquelme, 2017. "Recent Advances in Energy Time Series Forecasting," Energies, MDPI, vol. 10(6), pages 1-3, June.
    13. Nailya Maitanova & Jan-Simon Telle & Benedikt Hanke & Matthias Grottke & Thomas Schmidt & Karsten von Maydell & Carsten Agert, 2020. "A Machine Learning Approach to Low-Cost Photovoltaic Power Prediction Based on Publicly Available Weather Reports," Energies, MDPI, vol. 13(3), pages 1-23, February.
    14. María Del Carmen Ruiz-Abellón & Antonio Gabaldón & Antonio Guillamón, 2018. "Load Forecasting for a Campus University Using Ensemble Methods Based on Regression Trees," Energies, MDPI, vol. 11(8), pages 1-22, August.

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